Overview

Dataset statistics

Number of variables24
Number of observations9760
Missing cells1404
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory192.0 B

Variable types

Numeric17
Categorical5
Boolean2

Warnings

city has constant value "Willow" Constant
coord_X is highly correlated with bearingHigh correlation
bearing is highly correlated with coord_XHigh correlation
house_quality_index is highly correlated with baths and 2 other fieldsHigh correlation
age_since_construction is highly correlated with age_since_renovation and 2 other fieldsHigh correlation
age_since_renovation is highly correlated with age_since_construction and 2 other fieldsHigh correlation
floors is highly correlated with age_since_construction and 2 other fieldsHigh correlation
bedrooms is highly correlated with baths and 1 other fieldsHigh correlation
baths is highly correlated with house_quality_index and 6 other fieldsHigh correlation
living_m2 is highly correlated with house_quality_index and 4 other fieldsHigh correlation
living_vs_neighbors is highly correlated with living_m2High correlation
price is highly correlated with house_quality_index and 2 other fieldsHigh correlation
coord_X is highly correlated with bearingHigh correlation
bearing is highly correlated with coord_X and 1 other fieldsHigh correlation
house_quality_index is highly correlated with age_since_construction and 5 other fieldsHigh correlation
age_since_construction is highly correlated with house_quality_index and 3 other fieldsHigh correlation
age_since_renovation is highly correlated with house_quality_index and 3 other fieldsHigh correlation
floors is highly correlated with house_quality_index and 3 other fieldsHigh correlation
bedrooms is highly correlated with baths and 1 other fieldsHigh correlation
baths is highly correlated with house_quality_index and 5 other fieldsHigh correlation
living_m2 is highly correlated with house_quality_index and 4 other fieldsHigh correlation
living_vs_neighbors is highly correlated with living_m2High correlation
price is highly correlated with bearing and 2 other fieldsHigh correlation
coord_X is highly correlated with bearingHigh correlation
coord_Y is highly correlated with floorsHigh correlation
bearing is highly correlated with coord_XHigh correlation
age_since_construction is highly correlated with age_since_renovation and 2 other fieldsHigh correlation
age_since_renovation is highly correlated with age_since_construction and 2 other fieldsHigh correlation
floors is highly correlated with coord_Y and 2 other fieldsHigh correlation
baths is highly correlated with age_since_construction and 2 other fieldsHigh correlation
living_m2 is highly correlated with bathsHigh correlation
living_m2 is highly correlated with price and 3 other fieldsHigh correlation
house_state_index is highly correlated with age_since_renovation and 1 other fieldsHigh correlation
coord_X is highly correlated with dist and 2 other fieldsHigh correlation
price is highly correlated with living_m2 and 2 other fieldsHigh correlation
bedrooms is highly correlated with living_m2 and 1 other fieldsHigh correlation
viewsToPOI is highly correlated with price and 1 other fieldsHigh correlation
dist is highly correlated with coord_X and 3 other fieldsHigh correlation
age_since_renovation is highly correlated with house_state_index and 6 other fieldsHigh correlation
view_quality is highly correlated with viewsToPOIHigh correlation
lot_m2 is highly correlated with lot_vs_neighborsHigh correlation
baths is highly correlated with living_m2 and 5 other fieldsHigh correlation
house_quality_index is highly correlated with living_m2 and 5 other fieldsHigh correlation
postalcode is highly correlated with coord_X and 3 other fieldsHigh correlation
bearing is highly correlated with coord_X and 5 other fieldsHigh correlation
lot_vs_neighbors is highly correlated with lot_m2High correlation
floors is highly correlated with age_since_renovation and 3 other fieldsHigh correlation
age_since_construction is highly correlated with house_state_index and 6 other fieldsHigh correlation
coord_Y is highly correlated with dist and 4 other fieldsHigh correlation
house_state_index is highly correlated with cityHigh correlation
dow is highly correlated with cityHigh correlation
basement is highly correlated with cityHigh correlation
floors is highly correlated with cityHigh correlation
viewsToPOI is highly correlated with view_quality and 1 other fieldsHigh correlation
view_quality is highly correlated with viewsToPOI and 1 other fieldsHigh correlation
city is highly correlated with house_state_index and 5 other fieldsHigh correlation
coord_X has 428 (4.4%) missing values Missing
coord_Y has 976 (10.0%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
age_since_construction has 111 (1.1%) zeros Zeros
age_since_renovation has 149 (1.5%) zeros Zeros

Reproduction

Analysis started2021-08-22 12:11:11.804005
Analysis finished2021-08-22 12:11:49.758763
Duration37.95 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct9760
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4880.5
Minimum1
Maximum9760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:49.838320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile488.95
Q12440.75
median4880.5
Q37320.25
95-th percentile9272.05
Maximum9760
Range9759
Interquartile range (IQR)4879.5

Descriptive statistics

Standard deviation2817.613648
Coefficient of variation (CV)0.5773206941
Kurtosis-1.2
Mean4880.5
Median Absolute Deviation (MAD)2440
Skewness0
Sum47633680
Variance7938946.667
MonotonicityStrictly increasing
2021-08-22T14:11:49.976257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
65101
 
< 0.1%
65031
 
< 0.1%
65041
 
< 0.1%
65051
 
< 0.1%
65061
 
< 0.1%
65071
 
< 0.1%
65081
 
< 0.1%
65091
 
< 0.1%
65111
 
< 0.1%
Other values (9750)9750
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
97601
< 0.1%
97591
< 0.1%
97581
< 0.1%
97571
< 0.1%
97561
< 0.1%
97551
< 0.1%
97541
< 0.1%
97531
< 0.1%
97521
< 0.1%
97511
< 0.1%

city
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.4 KiB
Willow
9760 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters58560
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWillow
2nd rowWillow
3rd rowWillow
4th rowWillow
5th rowWillow

Common Values

ValueCountFrequency (%)
Willow9760
100.0%

Length

2021-08-22T14:11:50.152700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-22T14:11:50.205066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
willow9760
100.0%

Most occurring characters

ValueCountFrequency (%)
l19520
33.3%
W9760
16.7%
i9760
16.7%
o9760
16.7%
w9760
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter48800
83.3%
Uppercase Letter9760
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l19520
40.0%
i9760
20.0%
o9760
20.0%
w9760
20.0%
Uppercase Letter
ValueCountFrequency (%)
W9760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin58560
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l19520
33.3%
W9760
16.7%
i9760
16.7%
o9760
16.7%
w9760
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII58560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l19520
33.3%
W9760
16.7%
i9760
16.7%
o9760
16.7%
w9760
16.7%

postalcode
Real number (ℝ≥0)

HIGH CORRELATION

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10030.27715
Minimum10001
Maximum10059
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:50.270746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10004
Q110016
median10029
Q310046
95-th percentile10057
Maximum10059
Range58
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.21674664
Coefficient of variation (CV)0.001716477659
Kurtosis-1.262501646
Mean10030.27715
Median Absolute Deviation (MAD)16
Skewness-0.004808234373
Sum97895505
Variance296.4163649
MonotonicityNot monotonic
2021-08-22T14:11:50.393839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10009303
 
3.1%
10048286
 
2.9%
10054274
 
2.8%
10045272
 
2.8%
10057272
 
2.8%
10047266
 
2.7%
10010263
 
2.7%
10049263
 
2.7%
10028256
 
2.6%
10005238
 
2.4%
Other values (49)7067
72.4%
ValueCountFrequency (%)
10001193
2.0%
10002123
1.3%
10003121
 
1.2%
10004125
1.3%
10005238
2.4%
10006137
1.4%
10007154
1.6%
1000889
 
0.9%
10009303
3.1%
10010263
2.7%
ValueCountFrequency (%)
10059104
 
1.1%
10058195
2.0%
10057272
2.8%
10056137
1.4%
10055133
1.4%
10054274
2.8%
1005370
 
0.7%
10052155
1.6%
10051138
1.4%
10050127
1.3%

coord_X
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9186
Distinct (%)98.4%
Missing428
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean5386.42675
Minimum-29004.59741
Maximum29025.47585
Zeros0
Zeros (%)0.0%
Negative3056
Negative (%)31.3%
Memory size76.4 KiB
2021-08-22T14:11:50.525606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-29004.59741
5-th percentile-22251.64969
Q1-4482.997691
median6904.453639
Q318410.89927
95-th percentile26129.04306
Maximum29025.47585
Range58030.07326
Interquartile range (IQR)22893.89696

Descriptive statistics

Standard deviation15202.70332
Coefficient of variation (CV)2.82240974
Kurtosis-0.831938099
Mean5386.42675
Median Absolute Deviation (MAD)11488.75903
Skewness-0.4529847107
Sum50266134.43
Variance231122188.3
MonotonicityNot monotonic
2021-08-22T14:11:50.634934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16459.874574
 
< 0.1%
20266.103333
 
< 0.1%
189.08332323
 
< 0.1%
15921.986953
 
< 0.1%
23440.800283
 
< 0.1%
2632.8242733
 
< 0.1%
-10684.395763
 
< 0.1%
11281.713812
 
< 0.1%
-21033.364892
 
< 0.1%
4062.3518542
 
< 0.1%
Other values (9176)9304
95.3%
(Missing)428
 
4.4%
ValueCountFrequency (%)
-29004.597411
< 0.1%
-28822.50841
< 0.1%
-28810.257311
< 0.1%
-28783.539721
< 0.1%
-28764.93571
< 0.1%
-28759.567941
< 0.1%
-28739.512981
< 0.1%
-28728.39541
< 0.1%
-28728.312711
< 0.1%
-28713.396091
< 0.1%
ValueCountFrequency (%)
29025.475851
< 0.1%
29021.476671
< 0.1%
29008.025371
< 0.1%
29007.877571
< 0.1%
28977.828341
< 0.1%
28969.401961
< 0.1%
28956.441341
< 0.1%
28934.032221
< 0.1%
28925.968221
< 0.1%
28924.316231
< 0.1%

coord_Y
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8678
Distinct (%)98.8%
Missing976
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean3408.415067
Minimum-17244.59413
Maximum17460.78276
Zeros0
Zeros (%)0.0%
Negative3141
Negative (%)32.2%
Memory size76.4 KiB
2021-08-22T14:11:50.754716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-17244.59413
5-th percentile-12446.31181
Q1-2825.119447
median4545.527584
Q310888.48819
95-th percentile15306.44325
Maximum17460.78276
Range34705.37689
Interquartile range (IQR)13713.60764

Descriptive statistics

Standard deviation8824.422759
Coefficient of variation (CV)2.589010606
Kurtosis-0.9183771849
Mean3408.415067
Median Absolute Deviation (MAD)6835.877608
Skewness-0.4027123092
Sum29939517.95
Variance77870437.04
MonotonicityNot monotonic
2021-08-22T14:11:50.864575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
941.46548233
 
< 0.1%
13015.578583
 
< 0.1%
15320.691233
 
< 0.1%
-2816.7968163
 
< 0.1%
-9292.8358832
 
< 0.1%
13325.525092
 
< 0.1%
-14106.400842
 
< 0.1%
15250.303652
 
< 0.1%
15281.688172
 
< 0.1%
8010.7784662
 
< 0.1%
Other values (8668)8760
89.8%
(Missing)976
 
10.0%
ValueCountFrequency (%)
-17244.594131
< 0.1%
-17169.323561
< 0.1%
-17169.225231
< 0.1%
-17168.27571
< 0.1%
-17138.397211
< 0.1%
-17092.000061
< 0.1%
-17050.609911
< 0.1%
-17018.684431
< 0.1%
-16992.451041
< 0.1%
-16991.801241
< 0.1%
ValueCountFrequency (%)
17460.782761
< 0.1%
17416.760241
< 0.1%
17392.285791
< 0.1%
17391.290541
< 0.1%
17315.968161
< 0.1%
17242.160371
< 0.1%
17242.061851
< 0.1%
17241.338241
< 0.1%
17191.973911
< 0.1%
17191.483211
< 0.1%

dist
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9594
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17237.17687
Minimum411.578375
Maximum32637.53292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:50.978477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum411.578375
5-th percentile4585.92532
Q112190.54502
median17531.37781
Q322897.08524
95-th percentile28471.63358
Maximum32637.53292
Range32225.95454
Interquartile range (IQR)10706.54022

Descriptive statistics

Standard deviation7260.517326
Coefficient of variation (CV)0.4212126719
Kurtosis-0.7884961698
Mean17237.17687
Median Absolute Deviation (MAD)5348.867749
Skewness-0.2182537807
Sum168234846.2
Variance52715111.84
MonotonicityNot monotonic
2021-08-22T14:11:51.089018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18108.137514
 
< 0.1%
15545.267563
 
< 0.1%
22608.005823
 
< 0.1%
10684.462373
 
< 0.1%
960.2654623
 
< 0.1%
26811.870573
 
< 0.1%
16169.230433
 
< 0.1%
12092.166593
 
< 0.1%
14854.0272
 
< 0.1%
8858.879652
 
< 0.1%
Other values (9584)9731
99.7%
ValueCountFrequency (%)
411.5783751
< 0.1%
414.4069711
< 0.1%
415.5999731
< 0.1%
511.5972331
< 0.1%
531.8555591
< 0.1%
534.577991
< 0.1%
537.7177332
< 0.1%
567.2682951
< 0.1%
568.8258931
< 0.1%
575.7399451
< 0.1%
ValueCountFrequency (%)
32637.532921
< 0.1%
32548.054741
< 0.1%
32502.0591
< 0.1%
32391.451421
< 0.1%
32379.949281
< 0.1%
32352.819591
< 0.1%
32336.294861
< 0.1%
32241.719691
< 0.1%
32202.506851
< 0.1%
32190.427251
< 0.1%

bearing
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9618
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.85194373
Minimum-179.71236
Maximum179.724065
Zeros0
Zeros (%)0.0%
Negative3206
Negative (%)32.8%
Memory size76.4 KiB
2021-08-22T14:11:51.197222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-179.71236
5-th percentile-121.8942112
Q1-56.93703025
median50.696071
Q385.417036
95-th percentile142.4108734
Maximum179.724065
Range359.436425
Interquartile range (IQR)142.3540662

Descriptive statistics

Standard deviation83.54476941
Coefficient of variation (CV)3.361699604
Kurtosis-0.886973302
Mean24.85194373
Median Absolute Deviation (MAD)52.0749205
Skewness-0.4482134351
Sum242554.9708
Variance6979.728496
MonotonicityNot monotonic
2021-08-22T14:11:51.297168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.7509013
 
< 0.1%
11.3561673
 
< 0.1%
60.9586373
 
< 0.1%
100.0325273
 
< 0.1%
63.6904663
 
< 0.1%
65.3643163
 
< 0.1%
-93.0267132
 
< 0.1%
2.8292552
 
< 0.1%
44.4371442
 
< 0.1%
83.9416092
 
< 0.1%
Other values (9608)9734
99.7%
ValueCountFrequency (%)
-179.712361
< 0.1%
-178.9227941
< 0.1%
-178.7721311
< 0.1%
-177.8933941
< 0.1%
-177.1749221
< 0.1%
-173.3507931
< 0.1%
-172.6743641
< 0.1%
-172.0491121
< 0.1%
-169.8030091
< 0.1%
-169.7030591
< 0.1%
ValueCountFrequency (%)
179.7240651
< 0.1%
179.6278381
< 0.1%
179.5187381
< 0.1%
178.1274091
< 0.1%
176.8656971
< 0.1%
176.7862221
< 0.1%
176.6692061
< 0.1%
176.6107051
< 0.1%
176.5967611
< 0.1%
176.5809131
< 0.1%

house_quality_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.669159836
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:51.388268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median4
Q35
95-th percentile7
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.156151305
Coefficient of variation (CV)0.24761442
Kurtosis1.093004059
Mean4.669159836
Median Absolute Deviation (MAD)1
Skewness0.8068122457
Sum45571
Variance1.33668584
MonotonicityNot monotonic
2021-08-22T14:11:51.457931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
44090
41.9%
52818
28.9%
61132
 
11.6%
3856
 
8.8%
7541
 
5.5%
8170
 
1.7%
299
 
1.0%
943
 
0.4%
19
 
0.1%
102
 
< 0.1%
ValueCountFrequency (%)
19
 
0.1%
299
 
1.0%
3856
 
8.8%
44090
41.9%
52818
28.9%
61132
 
11.6%
7541
 
5.5%
8170
 
1.7%
943
 
0.4%
102
 
< 0.1%
ValueCountFrequency (%)
102
 
< 0.1%
943
 
0.4%
8170
 
1.7%
7541
 
5.5%
61132
 
11.6%
52818
28.9%
44090
41.9%
3856
 
8.8%
299
 
1.0%
19
 
0.1%

house_state_index
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.4 KiB
OK
6328 
Satisfied
2599 
Very Satisfied
751 
Disatisfied
 
67
Very Disatisfied
 
15

Length

Max length16
Median length2
Mean length4.870696721
Min length2

Characters and Unicode

Total characters47538
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery Satisfied
2nd rowOK
3rd rowSatisfied
4th rowOK
5th rowOK

Common Values

ValueCountFrequency (%)
OK6328
64.8%
Satisfied2599
26.6%
Very Satisfied751
 
7.7%
Disatisfied67
 
0.7%
Very Disatisfied15
 
0.2%

Length

2021-08-22T14:11:51.625840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-22T14:11:51.691742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ok6328
60.1%
satisfied3350
31.8%
very766
 
7.3%
disatisfied82
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i6946
14.6%
O6328
13.3%
K6328
13.3%
e4198
8.8%
s3514
7.4%
a3432
7.2%
t3432
7.2%
f3432
7.2%
d3432
7.2%
S3350
7.0%
Other values (5)3146
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29918
62.9%
Uppercase Letter16854
35.5%
Space Separator766
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i6946
23.2%
e4198
14.0%
s3514
11.7%
a3432
11.5%
t3432
11.5%
f3432
11.5%
d3432
11.5%
r766
 
2.6%
y766
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
O6328
37.5%
K6328
37.5%
S3350
19.9%
V766
 
4.5%
D82
 
0.5%
Space Separator
ValueCountFrequency (%)
766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin46772
98.4%
Common766
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i6946
14.9%
O6328
13.5%
K6328
13.5%
e4198
9.0%
s3514
7.5%
a3432
7.3%
t3432
7.3%
f3432
7.3%
d3432
7.3%
S3350
7.2%
Other values (4)2380
 
5.1%
Common
ValueCountFrequency (%)
766
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII47538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i6946
14.6%
O6328
13.3%
K6328
13.3%
e4198
8.8%
s3514
7.4%
a3432
7.2%
t3432
7.2%
f3432
7.2%
d3432
7.2%
S3350
7.0%
Other values (5)3146
6.6%

age_since_construction
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct117
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.94743852
Minimum0
Maximum116
Zeros111
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:52.081876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q119
median41
Q363
95-th percentile99
Maximum116
Range116
Interquartile range (IQR)44

Descriptive statistics

Standard deviation28.96736636
Coefficient of variation (CV)0.6591366263
Kurtosis-0.6256454176
Mean43.94743852
Median Absolute Deviation (MAD)22
Skewness0.4475693933
Sum428927
Variance839.108314
MonotonicityNot monotonic
2021-08-22T14:11:52.183388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11206
 
2.1%
9185
 
1.9%
10179
 
1.8%
12175
 
1.8%
38165
 
1.7%
47162
 
1.7%
8160
 
1.6%
48160
 
1.6%
36157
 
1.6%
37153
 
1.6%
Other values (107)8058
82.6%
ValueCountFrequency (%)
0111
1.1%
1122
1.2%
2150
1.5%
372
 
0.7%
462
 
0.6%
583
0.9%
6122
1.2%
7148
1.5%
8160
1.6%
9185
1.9%
ValueCountFrequency (%)
1166
 
0.1%
11519
0.2%
11419
0.2%
11315
 
0.2%
11217
0.2%
11125
0.3%
11027
0.3%
10930
0.3%
10840
0.4%
10737
0.4%

age_since_renovation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct117
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.45696721
Minimum0
Maximum116
Zeros149
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:52.287119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median38
Q361
95-th percentile97
Maximum116
Range116
Interquartile range (IQR)45

Descriptive statistics

Standard deviation28.50175842
Coefficient of variation (CV)0.6875022545
Kurtosis-0.518947199
Mean41.45696721
Median Absolute Deviation (MAD)22
Skewness0.5270992055
Sum404620
Variance812.3502332
MonotonicityNot monotonic
2021-08-22T14:11:52.393613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11205
 
2.1%
10198
 
2.0%
12191
 
2.0%
9183
 
1.9%
2173
 
1.8%
8171
 
1.8%
36168
 
1.7%
47161
 
1.6%
38160
 
1.6%
7156
 
1.6%
Other values (107)7994
81.9%
ValueCountFrequency (%)
0149
1.5%
1150
1.5%
2173
1.8%
371
 
0.7%
491
0.9%
5111
1.1%
6131
1.3%
7156
1.6%
8171
1.8%
9183
1.9%
ValueCountFrequency (%)
1164
 
< 0.1%
11516
0.2%
1148
 
0.1%
11317
0.2%
11219
0.2%
11117
0.2%
11020
0.2%
10925
0.3%
10832
0.3%
10733
0.3%

floors
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.4 KiB
1
4889 
3
3722 
2
808 
5
 
277
4
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9760
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
14889
50.1%
33722
38.1%
2808
 
8.3%
5277
 
2.8%
464
 
0.7%

Length

2021-08-22T14:11:52.569862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-22T14:11:52.627449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
14889
50.1%
33722
38.1%
2808
 
8.3%
5277
 
2.8%
464
 
0.7%

Most occurring characters

ValueCountFrequency (%)
14889
50.1%
33722
38.1%
2808
 
8.3%
5277
 
2.8%
464
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9760
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14889
50.1%
33722
38.1%
2808
 
8.3%
5277
 
2.8%
464
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common9760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14889
50.1%
33722
38.1%
2808
 
8.3%
5277
 
2.8%
464
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII9760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14889
50.1%
33722
38.1%
2808
 
8.3%
5277
 
2.8%
464
 
0.7%

basement
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
False
5806 
True
3954 
ValueCountFrequency (%)
False5806
59.5%
True3954
40.5%
2021-08-22T14:11:52.671765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.379713115
Minimum0
Maximum8
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:52.720416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8954338287
Coefficient of variation (CV)0.2649437388
Kurtosis1.016137645
Mean3.379713115
Median Absolute Deviation (MAD)1
Skewness0.3974428781
Sum32986
Variance0.8018017416
MonotonicityNot monotonic
2021-08-22T14:11:52.795059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
34385
44.9%
43192
32.7%
21225
 
12.6%
5727
 
7.4%
6119
 
1.2%
182
 
0.8%
718
 
0.2%
87
 
0.1%
05
 
0.1%
ValueCountFrequency (%)
05
 
0.1%
182
 
0.8%
21225
 
12.6%
34385
44.9%
43192
32.7%
5727
 
7.4%
6119
 
1.2%
718
 
0.2%
87
 
0.1%
ValueCountFrequency (%)
87
 
0.1%
718
 
0.2%
6119
 
1.2%
5727
 
7.4%
43192
32.7%
34385
44.9%
21225
 
12.6%
182
 
0.8%
05
 
0.1%

baths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.106147541
Minimum0
Maximum6
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:52.873383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median2.25
Q32.5
95-th percentile3.5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7571367686
Coefficient of variation (CV)0.3594889503
Kurtosis0.4361242079
Mean2.106147541
Median Absolute Deviation (MAD)0.5
Skewness0.3776579914
Sum20556
Variance0.5732560864
MonotonicityNot monotonic
2021-08-22T14:11:52.952523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2.52453
25.1%
11751
17.9%
1.751378
14.1%
2.25929
 
9.5%
2872
 
8.9%
1.5666
 
6.8%
2.75520
 
5.3%
3.5329
 
3.4%
3319
 
3.3%
3.25266
 
2.7%
Other values (14)277
 
2.8%
ValueCountFrequency (%)
04
 
< 0.1%
0.53
 
< 0.1%
0.7527
 
0.3%
11751
17.9%
1.256
 
0.1%
1.5666
 
6.8%
1.751378
14.1%
2872
 
8.9%
2.25929
 
9.5%
2.52453
25.1%
ValueCountFrequency (%)
62
 
< 0.1%
5.751
 
< 0.1%
5.52
 
< 0.1%
5.254
 
< 0.1%
510
 
0.1%
4.7511
 
0.1%
4.545
0.5%
4.2538
0.4%
458
0.6%
3.7566
0.7%

living_m2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct474
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.8971311
Minimum34
Maximum929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:53.054112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile86
Q1133
median178
Q3237
95-th percentile347
Maximum929
Range895
Interquartile range (IQR)104

Descriptive statistics

Standard deviation83.1492181
Coefficient of variation (CV)0.4310547161
Kurtosis3.130476515
Mean192.8971311
Median Absolute Deviation (MAD)50
Skewness1.262838542
Sum1882676
Variance6913.792471
MonotonicityNot monotonic
2021-08-22T14:11:53.155356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14377
 
0.8%
16970
 
0.7%
16569
 
0.7%
15368
 
0.7%
13068
 
0.7%
17067
 
0.7%
14165
 
0.7%
15465
 
0.7%
13165
 
0.7%
13463
 
0.6%
Other values (464)9083
93.1%
ValueCountFrequency (%)
341
 
< 0.1%
362
< 0.1%
381
 
< 0.1%
392
< 0.1%
431
 
< 0.1%
482
< 0.1%
503
< 0.1%
511
 
< 0.1%
521
 
< 0.1%
531
 
< 0.1%
ValueCountFrequency (%)
9291
< 0.1%
7461
< 0.1%
7301
< 0.1%
7291
< 0.1%
7161
< 0.1%
7071
< 0.1%
6921
< 0.1%
6591
< 0.1%
6471
< 0.1%
6451
< 0.1%

lot_m2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2329
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1139.655738
Minimum53
Maximum42172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:53.266329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile171
Q1467
median697
Q3960
95-th percentile3269.05
Maximum42172
Range42119
Interquartile range (IQR)493

Descriptive statistics

Standard deviation2295.626589
Coefficient of variation (CV)2.014315826
Kurtosis93.57251416
Mean1139.655738
Median Absolute Deviation (MAD)235
Skewness8.4976194
Sum11123040
Variance5269901.437
MonotonicityNot monotonic
2021-08-22T14:11:53.370017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46553
 
0.5%
46647
 
0.5%
46344
 
0.5%
46440
 
0.4%
67039
 
0.4%
37139
 
0.4%
37238
 
0.4%
55837
 
0.4%
55935
 
0.4%
37334
 
0.3%
Other values (2319)9354
95.8%
ValueCountFrequency (%)
531
 
< 0.1%
592
 
< 0.1%
601
 
< 0.1%
611
 
< 0.1%
632
 
< 0.1%
642
 
< 0.1%
651
 
< 0.1%
661
 
< 0.1%
671
 
< 0.1%
696
0.1%
ValueCountFrequency (%)
421721
< 0.1%
412331
< 0.1%
403631
< 0.1%
390381
< 0.1%
384521
< 0.1%
334571
< 0.1%
313331
< 0.1%
293311
< 0.1%
275761
< 0.1%
272671
< 0.1%

living_vs_neighbors
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct247
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.053856557
Minimum0.18
Maximum5.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:53.476703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile0.63
Q10.88
median1
Q31.16
95-th percentile1.63
Maximum5.98
Range5.8
Interquartile range (IQR)0.28

Descriptive statistics

Standard deviation0.3228443751
Coefficient of variation (CV)0.3063456529
Kurtosis15.37590841
Mean1.053856557
Median Absolute Deviation (MAD)0.14
Skewness2.241772348
Sum10285.64
Variance0.1042284905
MonotonicityNot monotonic
2021-08-22T14:11:53.585729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1971
 
9.9%
1.01306
 
3.1%
0.99299
 
3.1%
0.98183
 
1.9%
0.95165
 
1.7%
0.94163
 
1.7%
0.96156
 
1.6%
1.06156
 
1.6%
1.02152
 
1.6%
1.03150
 
1.5%
Other values (237)7059
72.3%
ValueCountFrequency (%)
0.181
 
< 0.1%
0.271
 
< 0.1%
0.283
< 0.1%
0.293
< 0.1%
0.31
 
< 0.1%
0.311
 
< 0.1%
0.332
< 0.1%
0.341
 
< 0.1%
0.362
< 0.1%
0.372
< 0.1%
ValueCountFrequency (%)
5.981
< 0.1%
5.731
< 0.1%
3.541
< 0.1%
3.491
< 0.1%
3.361
< 0.1%
3.351
< 0.1%
3.251
< 0.1%
3.21
< 0.1%
3.191
< 0.1%
3.181
< 0.1%

lot_vs_neighbors
Real number (ℝ≥0)

HIGH CORRELATION

Distinct362
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.100652664
Minimum0.11
Maximum35.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:53.692116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile0.67
Q10.94
median1
Q31.09
95-th percentile1.71
Maximum35.44
Range35.33
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.7840001855
Coefficient of variation (CV)0.7123048089
Kurtosis642.5481528
Mean1.100652664
Median Absolute Deviation (MAD)0.07
Skewness19.82103316
Sum10742.37
Variance0.6146562909
MonotonicityNot monotonic
2021-08-22T14:11:53.799402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12324
23.8%
0.99338
 
3.5%
1.01278
 
2.8%
0.97220
 
2.3%
0.98214
 
2.2%
0.96186
 
1.9%
0.95181
 
1.9%
1.02172
 
1.8%
1.03168
 
1.7%
0.94167
 
1.7%
Other values (352)5512
56.5%
ValueCountFrequency (%)
0.111
 
< 0.1%
0.122
 
< 0.1%
0.131
 
< 0.1%
0.143
< 0.1%
0.151
 
< 0.1%
0.22
 
< 0.1%
0.212
 
< 0.1%
0.227
0.1%
0.234
< 0.1%
0.243
< 0.1%
ValueCountFrequency (%)
35.441
< 0.1%
28.041
< 0.1%
22.321
< 0.1%
19.551
< 0.1%
13.281
< 0.1%
12.511
< 0.1%
12.331
< 0.1%
11.221
< 0.1%
10.771
< 0.1%
10.531
< 0.1%

viewsToPOI
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
False
9691 
True
 
69
ValueCountFrequency (%)
False9691
99.3%
True69
 
0.7%
2021-08-22T14:11:53.871452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

view_quality
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.4 KiB
Very Disatisfied
8837 
OK
 
407
Satisfied
 
209
Disatisfied
 
166
Very Satisfied
 
141

Length

Max length16
Median length16
Mean length15.15235656
Min length2

Characters and Unicode

Total characters147887
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery Disatisfied
2nd rowVery Disatisfied
3rd rowVery Disatisfied
4th rowVery Disatisfied
5th rowVery Disatisfied

Common Values

ValueCountFrequency (%)
Very Disatisfied8837
90.5%
OK407
 
4.2%
Satisfied209
 
2.1%
Disatisfied166
 
1.7%
Very Satisfied141
 
1.4%

Length

2021-08-22T14:11:54.012074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-22T14:11:54.074591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
disatisfied9003
48.0%
very8978
47.9%
ok407
 
2.2%
satisfied350
 
1.9%

Most occurring characters

ValueCountFrequency (%)
i27709
18.7%
s18356
12.4%
e18331
12.4%
a9353
 
6.3%
t9353
 
6.3%
f9353
 
6.3%
d9353
 
6.3%
D9003
 
6.1%
V8978
 
6.1%
r8978
 
6.1%
Other values (5)19120
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter119764
81.0%
Uppercase Letter19145
 
12.9%
Space Separator8978
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i27709
23.1%
s18356
15.3%
e18331
15.3%
a9353
 
7.8%
t9353
 
7.8%
f9353
 
7.8%
d9353
 
7.8%
r8978
 
7.5%
y8978
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
D9003
47.0%
V8978
46.9%
O407
 
2.1%
K407
 
2.1%
S350
 
1.8%
Space Separator
ValueCountFrequency (%)
8978
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin138909
93.9%
Common8978
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i27709
19.9%
s18356
13.2%
e18331
13.2%
a9353
 
6.7%
t9353
 
6.7%
f9353
 
6.7%
d9353
 
6.7%
D9003
 
6.5%
V8978
 
6.5%
r8978
 
6.5%
Other values (4)10142
 
7.3%
Common
ValueCountFrequency (%)
8978
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII147887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i27709
18.7%
s18356
12.4%
e18331
12.4%
a9353
 
6.3%
t9353
 
6.3%
f9353
 
6.3%
d9353
 
6.3%
D9003
 
6.1%
V8978
 
6.1%
r8978
 
6.1%
Other values (5)19120
12.9%

dow
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.4 KiB
Tue
2125 
Wed
2032 
Mon
1878 
Thu
1857 
Fri
1631 
Other values (2)
237 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters29280
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTue
2nd rowThu
3rd rowWed
4th rowMon
5th rowWed

Common Values

ValueCountFrequency (%)
Tue2125
21.8%
Wed2032
20.8%
Mon1878
19.2%
Thu1857
19.0%
Fri1631
16.7%
Sat131
 
1.3%
Sun106
 
1.1%

Length

2021-08-22T14:11:54.247625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-22T14:11:54.306743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
tue2125
21.8%
wed2032
20.8%
mon1878
19.2%
thu1857
19.0%
fri1631
16.7%
sat131
 
1.3%
sun106
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e4157
14.2%
u4088
14.0%
T3982
13.6%
W2032
6.9%
d2032
6.9%
n1984
6.8%
M1878
6.4%
o1878
6.4%
h1857
6.3%
F1631
 
5.6%
Other values (5)3761
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19520
66.7%
Uppercase Letter9760
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4157
21.3%
u4088
20.9%
d2032
10.4%
n1984
10.2%
o1878
9.6%
h1857
9.5%
r1631
 
8.4%
i1631
 
8.4%
a131
 
0.7%
t131
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
T3982
40.8%
W2032
20.8%
M1878
19.2%
F1631
16.7%
S237
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin29280
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4157
14.2%
u4088
14.0%
T3982
13.6%
W2032
6.9%
d2032
6.9%
n1984
6.8%
M1878
6.4%
o1878
6.4%
h1857
6.3%
F1631
 
5.6%
Other values (5)3761
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII29280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4157
14.2%
u4088
14.0%
T3982
13.6%
W2032
6.9%
d2032
6.9%
n1984
6.8%
M1878
6.4%
o1878
6.4%
h1857
6.3%
F1631
 
5.6%
Other values (5)3761
12.8%

month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.732479508
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:54.383445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.226950376
Coefficient of variation (CV)0.4793108352
Kurtosis-1.094810286
Mean6.732479508
Median Absolute Deviation (MAD)3
Skewness-0.06114076883
Sum65709
Variance10.41320873
MonotonicityNot monotonic
2021-08-22T14:11:54.454159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
71092
11.2%
41072
11.0%
61064
10.9%
8954
9.8%
10918
9.4%
3905
9.3%
9867
8.9%
12757
7.8%
11692
7.1%
2644
6.6%
Other values (2)795
8.1%
ValueCountFrequency (%)
1474
4.9%
2644
6.6%
3905
9.3%
41072
11.0%
5321
 
3.3%
61064
10.9%
71092
11.2%
8954
9.8%
9867
8.9%
10918
9.4%
ValueCountFrequency (%)
12757
7.8%
11692
7.1%
10918
9.4%
9867
8.9%
8954
9.8%
71092
11.2%
61064
10.9%
5321
 
3.3%
41072
11.0%
3905
9.3%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9667
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483571.7111
Minimum72584
Maximum6312525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2021-08-22T14:11:54.543377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum72584
5-th percentile192016.95
Q1290942
median403556
Q3579834.5
95-th percentile1019623.65
Maximum6312525
Range6239941
Interquartile range (IQR)288892.5

Descriptive statistics

Standard deviation316475.3648
Coefficient of variation (CV)0.6544538432
Kurtosis27.72951287
Mean483571.7111
Median Absolute Deviation (MAD)134054.5
Skewness3.597930137
Sum4719659900
Variance1.001566565 × 1011
MonotonicityNot monotonic
2021-08-22T14:11:54.644493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7614952
 
< 0.1%
2625902
 
< 0.1%
2796062
 
< 0.1%
4690102
 
< 0.1%
2197242
 
< 0.1%
1803092
 
< 0.1%
3498762
 
< 0.1%
3500792
 
< 0.1%
6487862
 
< 0.1%
3974382
 
< 0.1%
Other values (9657)9740
99.8%
ValueCountFrequency (%)
725841
< 0.1%
751131
< 0.1%
764531
< 0.1%
806201
< 0.1%
806881
< 0.1%
807461
< 0.1%
810021
< 0.1%
826551
< 0.1%
852551
< 0.1%
854151
< 0.1%
ValueCountFrequency (%)
63125251
< 0.1%
45998351
< 0.1%
37812011
< 0.1%
34445671
< 0.1%
33990461
< 0.1%
30533051
< 0.1%
30509411
< 0.1%
30084261
< 0.1%
29567041
< 0.1%
29566501
< 0.1%

Interactions

2021-08-22T14:11:22.061140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.161160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.252447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.339125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.423389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.508371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.594685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.675196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.762080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.846887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:22.933698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.016198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.099912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.181911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.268806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.355663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.438096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.521553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.609772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.702060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.795627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:23.980164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.072619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.161712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.249633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.342210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.432423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.524376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.613324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.698569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.789757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.879877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:24.970715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.058621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.147754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.234536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.325662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.418848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.510109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.602753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.691333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.778434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.870245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:25.960338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.052981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.139474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.223337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.309597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.397291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.592048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.677592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.767191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.854934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:26.950184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.048955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.151072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.246706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.337952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.425639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.520843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.609728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.699493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.789504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.876362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:27.959422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.049947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.143721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.229686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.318058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.404250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.494640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.586778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.675290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.764324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.852220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:28.938099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.031798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.127070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.220032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.309426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.400483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.489837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.579357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.668654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.888070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:29.973924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.064434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.159181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.260053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.351104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.443529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.536106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.622261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.710651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.800349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.890029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:30.977739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.062717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.146279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.235581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.330138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.417443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.502954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.587401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.675192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.761373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.855938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-22T14:11:31.946134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-22T14:11:54.947669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-22T14:11:55.174355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-22T14:11:55.365272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-22T14:11:55.538340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-22T14:11:49.011674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-22T14:11:49.374998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-22T14:11:49.549364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-22T14:11:49.642495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idcitypostalcodecoord_Xcoord_Ydistbearinghouse_quality_indexhouse_state_indexage_since_constructionage_since_renovationfloorsbasementbedroomsbathsliving_m2lot_m2living_vs_neighborslot_vs_neighborsviewsToPOIview_qualitydowmonthprice
01Willow10023-13563.883-717.19413582.830-93.0274Very Satisfied41411No32.001236710.861.00NVery DisatisfiedTue6226487
12Willow1003615160.21314160.97720745.24846.9526OK1093Yes33.502564171.410.75NVery DisatisfiedThu12852083
23Willow100055471.000-3198.0936337.163120.3094Satisfied61611Yes31.501427361.010.81NVery DisatisfiedWed7405006
34Willow100117259.583-15910.38017488.332155.4747OK10103No43.753097460.891.00NVery DisatisfiedMon7720376
45Willow1003113608.913NaN13704.47883.2304OK63631No31.001089990.490.79NVery DisatisfiedWed12786720
56Willow1004919984.68813773.93924271.57155.4244OK7453Yes42.752495001.651.00NVery DisatisfiedThu3645034
67Willow10050-14779.838NaN17356.101-58.3824Satisfied32321No32.001214650.800.67NVery DisatisfiedMon9215477
78Willow1005416977.24611227.61020354.01956.5224Satisfied1041042No32.001365840.871.32NVery DisatisfiedMon12635726
89Willow100022899.65915319.99215591.99110.7184OK19193Yes22.001161100.980.96NVery DisatisfiedThu3305475
910Willow1005418969.39912199.75222553.75957.2544Satisfied93931Yes30.751322861.060.77NVery DisatisfiedMon10320007

Last rows

idcitypostalcodecoord_Xcoord_Ydistbearinghouse_quality_indexhouse_state_indexage_since_constructionage_since_renovationfloorsbasementbedroomsbathsliving_m2lot_m2living_vs_neighborslot_vs_neighborsviewsToPOIview_qualitydowmonthprice
97509751Willow1002621861.64412869.03625368.16159.5164Satisfied60601No42.002126712.080.95NVery DisatisfiedTue11450039
97519752Willow1005126873.788-3036.42027044.78496.4465Satisfied34341Yes42.252689301.200.95NVery DisatisfiedFri11551512
97529753Willow1004919620.744NaN24683.00052.6475Satisfied61611No31.001515580.771.00NSatisfiedWed8493437
97539754Willow1005418655.97811149.29421733.66759.1364OK57571Yes31.751784471.091.00NVery DisatisfiedThu6629650
97549755Willow100104862.5926810.7688368.47435.5254OK67671No31.502286711.281.05NVery DisatisfiedFri11495355
97559756Willow1001525177.758-8286.97626506.480108.2184Satisfied48481No31.001088881.000.90NVery DisatisfiedTue11292574
97569757Willow10057-17505.139-8121.07119297.193-114.8884Satisfied27271No31.751178920.770.97NVery DisatisfiedTue4261727
97579758Willow100137232.3978086.85910849.18741.8084Satisfied1031031Yes52.502654661.301.00NVery DisatisfiedSat8665147
97589759Willow1005419238.02813024.98323232.56255.9004OK88872No31.001183551.000.91NVery DisatisfiedFri3482569
97599760Willow1003110073.106639.11510093.36186.3704Satisfied58581Yes42.502338101.171.00NVery DisatisfiedWed8695811